Method for determining a substance concentration and detector arrangement

Abstract

The invention concerns a method for determining a substance concentration in a sample comprising liquid containing particles, in particular glucose in blood, wherein a refractive index of the liquid is dependent on a concentration of the substance dissolved therein and a density of particles in the liquid is substantially constant, wherein the liquid is modulated in its volume or pressure. The proposed method obtains a first signal during a first time period with an acquisition rate that is at least two times larger than a periodicity of the volume or pressure modulation of the liquid and determines a variability therefrom. The variability or its change thereof over time directly corresponds to the concentration of the substance or a change thereof.

Description

[0001] METHOD FOR DETERMINING A SUBSTANCE CONCENTRATION AND DETECTOR

[0002] ARRANGEMENT

[0003] The present application claims priority of German patent application DE 10 2024 117 757 . 8 dated June 24 , 2024 , the disclosure of which is incorporated herein by reference in its entirety .

[0004] The present invention relates to a method for determining a substance concentration in a sample containing particles in a liquid, in particular glucose in blood, wherein a refractive index of the liquid is dependent on a concentration of the substance dissolved therein . The invention also relates to a detector arrangement .

[0005] BACKGROUND

[0006] The current standard for blood glucose measurement often uses an invasive technique , in which a small amount of blood is drawn, and subsequent electrochemical analysis is performed using a handheld device . This method is not suitable for continuous monitoring because for each measurement , the finger must be pricked to obtain a fresh blood sample .

[0007] A more recently developed technology uses a button that sits on the skin and misses interstitial fluid in parts of the subcutaneous adipose tissue with a small , needle-like sensor . However , the needle penetrates the skin permanently . Such approach bears the risk of infection . Moreover, it may have to be removed under certain circumstances , e . g . during sport activities , swimming and the like .

[0008] Besides these invasive methods , there are also non-invasive methods based on optical IR measurements or Raman spectroscopy . While in the first case , a suitable choice of emitter and detector leads to difficulties , an approach based on Raman spectroscopy is challenging due to the very poor signal-to-noise ratio . Recently, optical measurements using portions of the visible spectrum have been proposed, in which the back scattered light is analyzed . However, depending on skin type , environment and other parameters , the signal / noise ratio may vary and degrade the overall signal quality significantly . In this respect , there is a need for a method that can detect a substance in a liquid in a simpler way and allows for a continuous measurement .

[0009] SUMMARY OF THE INVENTION

[0010] This and other obj ects are addressed by the subj ect matter of the independent claims . Features and further aspects of the proposed principles are outlined in the dependent claims .

[0011] Following some other ideas that are based on various parameters depending on the scattering of light within the tissue as well as the evaluation of the perfusion index, the inventor found that such approaches require relatively large computational effort . While modern devices like smart watches , mobiles and the like usually have enough computational power , it may nevertheless be suitable to provide methods and solution that are implemented with reduced computational efforts .

[0012] During the studies , it was observed that certain parameters influence the glucose measurement in blood . More particularly, it was found that not only the blood glucose concentration influences the so-called perfusion index , but also the noise of any measurement signal itself . It seems that the scattering introduces systematic noise in the overall noise , which relies on the glucose concentration .

[0013] As a result of such observations , the inventor filed several applications , some of which have already been published, the W02022258800 , WO2023104898 and the WO2024017955 among them.

[0014] The perfusion index or modulation depth is a result during so-called Photoplethysmography or PPG, i . e . , the optical measurement of volume changes of human blood, e . g . , due to the beating of the heart , at a certain point of the human body, for instance the fingertip , or some other suitable location . The volume changes are observable by signal variations during the heartbeats resulting from less or more scattering based on the different blood volume . The PPG measurement itself is performed during a short period of time , e . g . 10s in intervals every 2 or 5 minutes for example . Due to scattering and absorption of light , which is initially directed towards the skin and may subsequently propagate through portions of shallow- and / or deeper lying tissue , which in turn hosts a network of blood vessels , the amount of light that re-emerges at the skin-to- ambient interface some distance from the entry point will vary according to the optical path length ( distance ) travelled in blood . The path length is referred to as "blood optical path length or ( BOPL ) , " , and is directly affected by the beating of the heart . Consequently, the BOPL varies with time , while the properties of surrounding portions of tissue can be considered constant , at least on the timescale of the heart rate and the short measurement .

[0015] When the BOPL is at a maximum or minimum, the emerging signal after interaction is conversely at a minimum or maximum. The difference between minimum and maximum signal is called modulation, and the ratio of modulation to the average signal is defined as modulation depth, ac-to-dc ratio , or perfusion index, PI .

[0016] According to Beer-Lambert ' s law, the absorption of light in blood is largely determined by the presence of hemoglobin within the erythrocytes , as well as the cumulative BOPL . Under the assumption that the density of erythrocytes does not change during the measurement period, one can say that light propagating nominally through the more or less transparent blood plasma , undergoes scattering at the red blood cells , such that the cumulative BOPL from entry point to detection point , both of which are fixed by the device configuration, may depend on the scattering characteristics in the sense of a random walk, i . e . , it will become a stochastic process .

[0017] Consequently, the perfusion index or modulation depth is primarily a result of the direct interaction and particularly the scattering and absorption of light by erythrocytes . This principle is currently used for measurements of glucose concentration and / or the oxygen saturation in blood, also referred as SPO2 measurement . However , the mechanisms for both are different . While glucose changes the refractive index in the liquid containing the red blood cells , thus leading to a change in the scattering behavior ( the refractive index of the liquid is changing ) , Oxygen is binding to the hemoglobin within the erythrocytes , thus changing the optical properties of the scattering particles themselves . Hence , one can detect concentration changes of substances dissolved within in the ambient medium of the scattering particles , or can detect chemical changes of the scattering particles themselves - if they lead to a change in their optical properties -- under otherwise constant concentrations of the substances dissolved in the surrounding medium.

[0018] It has now been observed that for a human test subj ect under steadystate conditions and constant blood glucose levels , the perfusion index remains substantially constant . When the blood glucose level increases , the properties of the blood plasma and more particularly the refractive index of the plasma changes . As a result , the difference between the refractive indices of the erythrocytes and the blood plasma decreases , which will cause a decrease of scattering light . Likewise , a decrease in the glucose increases the difference between the refractive indices of the erythrocytes and the blood plasma causes an increase in scattering . This is explained in a model , in which scattering is mainly caused by differences in the index of refraction, as well as the relative dimensions of the wavelength of light and size of the scattering particle (Mie theory) . Thus , in a transparent medium, in which the refractive indices of the liquid and the scattering particles were the same , no scattering would be observable , since light would simply propagate through the medium without interaction . However, once there is an index mismatch, some light is being scattered ( e . g . , backwards ) by the particles and thus can be distinguished from the surrounding ambient , i . e . , plasma .

[0019] Consequently, one can use the perfusion index for a so-called Oral Glucose Tolerance Test (OGTT ) , in which a test subj ect starts with a baseline blood glucose level established by not eating for several hours followed by ingesting a liquid or substance of caloric value . Usually, changes in the perfusion index become visible after around 5 to 15 minutes after the intake . The inventor now proposes to utilize the total PPG signal variability over a frequency range relevant for PPG measurement , as such variability also correlates with the glucose concentration in blood . This principle can be generalized such that the variability of a measurement signal can be used to determine the concentration or the change of concentration of a substance in a liquid, said liquid containing particles suitable for refractive index dependent scattering, i . e . Mie-scattering . The benefit of such approach lies in the fact that the determination of the variability of a signal measured over a certain period of time is computationally much easier and cheaper to accomplish than other evaluation methods . The proposed approach can be implemented in the time or frequency domain and is therefore easier to realize than extracting only certain features from a signal , such as the PI , in the time- and / or frequency domain .

[0020] The proposed principle utilizes an algorithm for determining the signal variability .

[0021] The signal variability can be derived from the modulation of the signal due to the heartbeat as well as other sources of noise or variation, which are not directly attributable to the heartbeat . It is the „other sources" that are commonly referred to as noise and this noise also correlates with the glucose concentration . The signal variability can be determined using various relatively simple processing steps ( compared to other approaches ) both in the time and frequency domain, some of which are presented below . Furthermore , it seems that the overall signal variability, that is used for the proposed method, is bit more stable / sensitive than evaluation of the perfusion index alone and a lot more stable than evaluation of the noise .

[0022] Apparently, the tissue becomes more transparent as the glucose level increases or , more generally, the concentration of the substance to be measured increases . This is evidenced by the decreasing DC level during the same period when using a measurement configuration in reflection mode . The observed behavior may indicate that the detected light has interacted with deeper lying tissue layers and that various physiological mechanisms , such as blood flow, muscle activity, etc . , could add relatively more noise to the system. This interaction with deeper layers is in contrast with superficial layers , where there is little , or no blood flow and the s kin is composed of rather passive material layers .

[0023] As a result of this observation, the inventor proposes a novel method for determining a substance concentration in a sample comprising liquid containing particles , in particular glucose in blood, wherein a refractive index of the liquid is dependent on a concentration of the substance dissolved therein and a density of particles in the liquid is substantially constant . The proposed method is based on the extraction of the variability in the PPG data , according to Parseval' s theorem, to be performed equivalently in the time- or frequency domain .

[0024] While the method was developed mainly for glucose measurement , it is not limited thereon, but can be generalized into a method for determining a substance dissolved in a liquid containing particles as long as the liquid is excited by a pressure or volume modulation . The expression "dissolved in the liquid containing particles" includes the situation that the substance is directly dissolved in the liquid or bound somewhat to the particles thereby changing the optical behavior .

[0025] By obtaining the standard deviation from the signal in the time or frequency domain and forming a ratio thereof with the average signal level , one can obtain the variability and subsequently the change of the substance level and if a base reference is known also the overall absolute value .

[0026] The variability of the overall obtained signals can be quantified using the standard deviation, the variance , the range as well as the interquartile range . Those quantifications can be determined in the time domain time domain, the time-frequency domain using a wavelet transformation as well as in the frequency domain using one of Fourier, Laplace and Z-Transf ormation depending for example on the type of the measured signal and implementation . In some aspects , a method is proposed for determining a substance concentration in a sample comprising liquid containing particles , in particular glucose in blood, wherein a refractive index of the liquid is dependent on a concentration of the substance dissolved therein and a density of particles in the liquid is substantially constant . In the proposed method, the liquid is modulated in its volume or in its density . If the liquid is blood, with a substance concentration in blood to be measured, the heartbeat provides the required modulation .

[0027] For determining the substance concentration, a first signal is obtained during a first time period . The acquisition rate of the first obtained signal is set to be at least two times larger than the periodicity or frequency of the liquid ' s modulation to fulfil the Nyquist criteria . For example , the acquisition rate may be 10 times , 20 times or even 100 times higher . Usually, the acquisition rate is at least 40 times higher than the modulation frequency of the above-mentioned modulation . In the case of blood with a heartbeat of approximately 1 Hz to 2 Hz , the acquisition rate may be at least 10 Hz , and more particularly between 20 Hz and 200 Hz .

[0028] Likewise , the first time period is adj usted that a plurality of periodicity of the liquid volume or pressure modulation fit into the first time period . For example , the first time period may be between 3 times and 30 times larger than the time period of the liquid' s modulation . In case of heartbeat with a frequency of 1 Hz , the acquisition time may be between 5 seconds and 20 seconds .

[0029] This approach ensures that noise with a sufficient resolution and enough signal peaks are captured during the first time period and the acquisition of the first signal . In a second subsequent step , a variability from the obtained first signal is determined . As outlined further below, the step can be conducted in the time or frequency domain, including but not limited to certain pre-processing steps .

[0030] In a final step, the substance concentration is estimated and determined based on the determined variability and a reference value . Alternatively, a change in the substance concentration can be determined based on the determined variability and a reference value . In this regard, the reference value may be zero or any other base value . For example , the reference value may correspond to a ground or normal level of the substance concentration in the liquid .

[0031] In some aspects , the reference value is based on a processed second signal . Consequently, the proposed method includes the step of obtaining a second signal during a second time period with an acquisition rate that is at least two times larger than a periodicity of the volume or pressure modulation of the liquid . The second time period can be after or prior to the first time period, with a certain time gap between the measurements . Further measurements can be performed to obtain a plurality of such signals . It is suitable that the first and second time periods are the same to reduce possible residual effects , although the subsequent method step can compensate for different periodic measurement , e . g . by normalization .

[0032] A variability is determined from the obtained second signal , wherein optionally, the reference value is based on the determined variability of the obtained second signal . This allows to obtain a change in substance concentration from two or more measurements . Hence , in some aspects , the previously mentioned reference value is given by a predetermined variability, or a value derived by one or more variability .

[0033] In some cases , in which the substance concentration changes over time , one can obtain the change rate and other parameters of the substance concentration by evaluating at least two of the obtained signals in accordance with the proposed method , in some cases , the reference value is given in some aspects by a pre-determined estimated variability, or a value derived by such estimated variability .

[0034] In some aspects , the first and / or second time period is at least 5 times smaller than a time period between the first and the second time period . This allows that some sufficient time passes between two consecutive measurements in order to obtain a variability that allows to obtain a potential difference in the substance concentration, if any . It is also possible to continuously gather data and thus obtain the variability over a longer period of time . However, each variability point is measured using the above-mentioned acquisition time .

[0035] Some further aspects concern the step of determining the variability from the obtained first signal and / or the second obtained signal . The variability is defined as the ratio between the standard deviation of the signal and the average signal level , also referred to as DC component . Consequently, the inventor proposes to determine a standard deviation of the signal obtained during the acquisition period in the time domain . The average signal level or DC portion is also measured and determined during said acquisition period in the time domain . The variability can then be calculated and obtained based on a ratio between the standard deviation and the average signal level or DC portion . This approach using the signals in the time domain is particularly easy and does not include a high computational effort . It may be useful in applications requiring low and very low power consumption .

[0036] In some alternative , the step of determining the variability from the obtained first signal and / or the second obtained signal comprises the step of transforming the obtained first and / or second signal into the frequency domain . This approach is based on the use of Parseval' s theorem, wherein the standard deviation can be calculated in both the time- and the frequency domain .

[0037] Consequently, the standard deviation of the obtained signal is determined in the frequency domain . Likewise , the average signal level or DC portion of the obtained signal is determined in the frequency domain . The variability is then determined based on a ratio of the standard deviation and the average signal level or DC portion .

[0038] With regard to the term "portion" , it is understood that the overall signal spectrum itself is band limited due to the real-world signal processing . It contains a noise part , the actual signal referred to as AC part and the DC part . For the proposed method, a portion of the overall signal is obtained, and the variability obtained therefrom. The band limitation is possible in the time and in the frequency domain and is present either as a limitation due to the implementation of the measurement device or implemented deliberately, e . g . by restricting the signal to a certain frequency range . The reason for this approach lies in the fact that systematic noise (which is the interesting portion for the proposed method) decays e . g . , with 1 / f and higher frequency noise contribute less to the signal .

[0039] Consequently, band limitation can be implemented in some aspects , by low pass filtering the first and second signal in the time domain prior to determining one of the standard deviation and the average signal level or DC portion . The step of low pass filtering the first and / or second signal can also be conducted in the frequency domain . The cutoff frequency of the low-pass filter is adj usted to higher frequencies which include the fundamental of the heartbeat and up to 12 to 15 higher harmonics , for example . The cut-off frequency of the low-pass filter may be adj ustable and is set to a value , in which the maj ority of information of the signal is present , i . e . the cut-off portion do not change the information of interest in a significant way .

[0040] In some further aspect , the step of obtaining the variability also comprises high pass filtering the first and / or second signal in the time domain . Alternatively, the step of high pass filtering the first and / or second signal can also be conducted in the frequency domain . In some further aspects , the step of high-pass filtering the first and / or second signal removes transients , in particular with a frequency below 2 Hz and in particular below 1 Hz and in particular below 0 , 5 Hz . Usually these transients are caused by breathing, slow movement , or other disturbances , and may not be relevant for the signal and its variability that is being discussed here .

[0041] Further aspects include the removal of dark counts from the first and / or second signal . The measured and / or obtained first and / or second signal can also be converted into a digital signal before further processing them . It is possible in this regard to conduct certain processing steps in one domain ( e . g . time or frequency) and other processing steps in a different domain . For example , the signal may be converted into frequency domain, filtered, digitized and then transformed back into the time domain . By obtaining the standard deviation from the noise spectrum and correlating it to the blood Glucose or generally to the substance concentration in the liquid, one can obtain the change of the substance level and if a base reference is known also the overall absolute value .

[0042] Some aspects concern a detector arrangement . The detector arrangement may be implemented in a single housing, like a watch or medical instrument , but may also comprise distributed components . In some aspects , a detector arrangement is proposed for determining a substance concentration ( i . e . glucose ) in a sample comprising liquid containing particles ( i . e . blood ) , wherein a refractive index of the liquid is dependent on a concentration of the substance dissolved therein and a density of particles in the liquid is substantially constant .

[0043] The detector arrangement comprises at least one light source and at least one detecting component , wherein the at least one detecting component is optically separated from the at least one light source . The at least one light source is configured to emit light through an exit window onto a sample containing the liquid with the substance .

[0044] The detecting component is configured to detect a light component corresponding to emitted light scattered through the liquid containing particles . The detector arrangement further comprises a control circuit coupled to the at least one light source and at least one detecting component . The detector arrangement is configured to perform the above- mentioned proposed method .

[0045] In some aspects , the control circuit is configured to control the at least one light source to emit a light signal for the first time period at a plurality of different consecutive times and obtain the signals from the at least one detecting component . The control circuit can be controlled and triggered by the evaluation circuit .

[0046] Some aspects concern implementations of the detector arrangement . The detector arrangement may comprise a plurality of photodetectors arranged in a ring shape or a quadrature shape and optionally arranged, -particularly centrally- , around the at least one light source , in particular at different distances . In some other aspects , said at least one light source comprises a plurality of optoelectronic devices being arranged at different distances to the at least one detecting component ; and optionally comprising a ring shape or a quadrature shape optionally arranged, -particularly centrally- , around the at least one detecting component . It should be noted in this regard that one can also implement a linear arrangement of detectors having different distances to the light source . In some aspect , one can implement a linear arrangement having several light sources arranged at different distances to the photodetector .

[0047] It may be suitable if the at least one light source is configured to emit light of different wavelength . Different wavelength can be used for different applications and measurements and potentially increase the accuracy of the present method . In some other aspects , the at least one detecting component comprises a light filter comprising a low transmittance in a frequency spectrum different from a light spectrum emitted by the at least one light source .

[0048] Although the optoelectronic device presented herein is only illustrated with regard to its functionality concerning the determination of substance concentration, one may note that various implementations are possible . In this respect , the evaluation unit does not need to be implemented within the housing itself containing the emitter and the detector, but can be located separately therefrom . In some aspects , the evaluation unit is implemented in a separate device distanced from the housing itself . Communication between the emitter ( s ) , detector ( s ) and the evaluation unit as described above is facilitated for example by a wireless communication . This will allow for example to realize a master slave configuration, in which the evaluation unit requests measurements to be taken on regular basis . Furthermore , the evaluation unit may be implemented largely in Software , for example as an app executed on a mobile device , whereas the remaining portion of the optoelectronic device are implemented in a separate housing .

[0049] In some aspects , the housing ( or optoelectronic device in more general terms ) is implemented as a ring, earbud, watch, patch or any other wearable , that may be compatible in some aspects with a user' s daily routine and can be carried continuously. These mentioned wearables can also be part of a medical device and used during short or long term medical applications. Said ring, earbud, watch or any other wearable is in communicative connection with the evaluation unit, a mobile, a medical device or any other device implementing the evaluation unit. The ring, earbud, watch or any other wearable may cover a larger portion of the user's skin, e.g. , wrap around the finger, clipped to- or inserted into the ear, and measuring at one-, opposite-, or multiple sites, and the like; it is also conceivable to measure on opposite sides of the wrist, such as the dorsal (top) and palmar side near the clasp of the wrist strap. They may contain several emitters and detectors at various locations, thus allowing not only to measure at one spot but at several at the same instant or sequentially. As a result thereof, skin irregularity or other issues can be overcome and the overall measurement quality may be improved.

[0050] Apart from wearable- and handheld devices, other applications are possible. For example, the optoelectronic device can be implemented in medical devices or laboratory equipment, e.g. for test and measurement purposes. Those devices again can be stationary or mobile.

[0051] Some more aspects concern mobile displays in which the detectors are directly implemented. In such applications, the display LEDs e.g. for the blue, red and green color can be used as emitter in accordance with the proposed principle. A finger is placed directly on the display surface and then illuminated by the display for obtaining the first and / or second signal. Likewise, the proposed principle can be implemented in VR or AR glasses and devices. Another application concerns safety issues, e.g. during certain laborious- or dangerous work, while driving a motor vehicle, and the like. It is possible to implement such optoelectronic devices in accordance with the proposed principle in a car, e.g. on the steering wheel to obtain the perfusion index and from there the noise spectrum during driving. This enables for example to warn drivers of potential health threats while driving.

[0052] SHORT DESCRIPTION OF THE DRAWINGS Further aspects and embodiments in accordance with the proposed principle will become apparent in relation to the various embodiments and examples described in detail in connection with the accompanying drawings , in which

[0053] Figure 1 shows a detector arrangement in accordance with some aspects of the proposed principle ;

[0054] Figure 2 illustrates an exemplary PPG measurement that can be obtained by a detector arrangement of Figure 1 ;

[0055] Figure 3 illustrates the two PPG signals for point #1 and #30 corresponding to the respective points in Figure 4 ;

[0056] Figure 4 is a reference curve showing the glucose concentration over time as well as the corresponding signal variability obtained with light in the green spectrum, with two points selected to illustrate some aspects of the proposed principle ;

[0057] Figure 5 illustrates another diagram with the reference curve and the signal variability obtained with light in the infrared spectrum according to some aspects of the proposed principle ;

[0058] Figure 6 shows the Fourier transformation of the measured signals corresponding to data points #1 and #31 illustrating some aspects of the proposed principle ;

[0059] Figure 7 shows the fundamental and harmonics of the measured signals corresponding to data points #1 and #31 illustrating some aspects of the proposed principle ;

[0060] Figure 8 shows the noise portions between the fundamentals and the harmonics of the measured signals corresponding to data points #1 and #31 illustrating some aspects of the proposed principle ;

[0061] Figures 9A to 9D illustrate some correlation of the individual signal portions . DETAILED DESCRIPTION

[0062] The following embodiments and examples disclose various aspects and their combinations according to the proposed principle . The embodiments and examples are not always to scale . Likewise , different elements can be displayed enlarged or reduced in size to emphasize individual aspects . It goes without saying that the individual aspects of the embodiments and examples shown in the figures can be combined with each other without further ado , without this contradicting the principle according to the invention . Some aspects show a regular structure or form. It should be noted that in practice slight differences and deviations from the ideal form may occur without , however, contradicting the inventive idea .

[0063] In addition, the individual figures and aspects are not necessarily shown in the correct size , nor do the proportions between individual elements have to be essentially correct . Some aspects are highlighted by showing them enlarged . However , terms such as "above" , "over" , "below" , "under" "larger" , "smaller" and the like are correctly represented with regard to the elements in the figures . So it is possible to deduce such relations between the elements based on the figures .

[0064] Figure 1 illustrates a detector arrangement in accordance with some aspects of the proposed principle . In this embodiment , the detector arrangement is facilitated for glucose measurements in blood vessels of human or animal body . However, it is understood that the present invention and the proposed method is not limited to such measurements . Rather, it is possible for example to replace the human tissue by an artificial pipe to measure a substance concentration in a liquid flowing through the pipe . The various elements of such a detector arrangement might be similar to the arrangement presented in Figure 1 . This enables the proposed method for a variety of measurements in different applications .

[0065] The arrangement is used for a variety of measurements . To this extent , one can implement the proposed method as explained herein by software in already existing hardware , provided the detector arrangement is suitable to provide a PPG signal including the noise portion . In the present embodiment , the detector is part of a finger clip , being attached and clamped to a finger 30 or another body tissue with a perfused area . In some aspects , the detector is part of a smart watch being wrapped around someone' s wrist .

[0066] In the present embodiment , the detector arrangement 1 comprises a housing 11 including at least one light source 12 , said light source 12 configured to emit light pulses of at least one wavelength in the visible or IR spectrum.

[0067] More particularly, the light source 12 may also comprise a plurality of light generating components ( not show here ) that are configured to emit light of different wavelength or located at different position . This enables a compensation due to s kin irregularities and also provides an adj ustment to the signal-to-noise ratio , as the penetration of light into the deeper layer strongly depends on the wavelength . For example , typical wavelengths suitable for PPG measurements and measurement for determining the glucose concentration include light in the green and red visible spectrum as well as in the near infrared spectrum. Usually, there are all three light sources available , as the emission and detection of light at different wavelength can be used for different applications .

[0068] Housing 11 further comprises a detector device 13 that is arranged separated from the light source 12 . A light blocking element 14 is arranged between the light source 12 and the photodetector . In this example , the photodetector 13 comprises several detector areas 13 . 1 , 13 . 2 etc . The detector areas are located at different distances from the light source 12 . The various distances between the light source 12 and the detector areas 13 . 1 , 13 . 2 can compensate skin irregularities , but also provide distance dependent signals . Those signals can also be used to determine the substance concentration, because the overall amount of scattering is dependent from distance .

[0069] In an alternative or in an additional aspect , the detector elements

[0070] 13 . 1 to 13 . 3 are configured to detect light of different wavelength . This can be achieved by mechanical means, e.g. using color filter on top of the detector surface, in the manufacturing process by selecting materials which are wavelength selective, or electrically by simply switching those elements off, which are not use. In some embodiments, the detector elements are sensitive to light of all colors, and thus will detect the scattered light from various distances to the source.

[0071] In the present invention, in which systematic noise is added due to scattering the emitted light within the skin of a user, a plurality of detector areas 13.1, 13.2 at various distances from the light source 12 enables to determine an optimal distance. This systematic noise is relevant for the variability of the detected signal.

[0072] Finally, the detector arrangement 1 also comprises an evaluation and control device 10. The evaluation device 10 can be placed within the housing 11 of the arrangement or located separately. For example, the housing 11 can be part of a smart watch or a wearable device communicating with another device via a wireless interface. The other device contains the evaluation and control device 10. The evaluation and control device 10 is implemented as hardware, software, or a combination of both.

[0073] The evaluation and control device 10 is controlling the measurement and can be referred to as an integrated data acquisition system, which may provide control signals to the current drivers for the at least one light source 12, the analogue to digital converter (s) for sampling of the current from the detector device 13 and the areas 13.1, 13.2, as well as clocks for triggering and timing of measurements .

[0074] During a measurement, a user places its finger with tissue 30 on surface 20, that is part of a glass interface 21 or another transparent interface 21. The interface 21 is transparent for light emitted by the light source, but may be opaque or at least comprises a reduced transparency for another wavelength. This reduces the portion of current caused by ambient light at the photodetector 13 and its areas 13.1 and 13.2. The light emitted by light source 11 propagates through tissue 30 along various light paths 32 in tissue 30, where interaction with various blood-carrying and non-blood carrying layers and vessels occur. Due to the resulting, periodic blood flow, the heartbeat modulates the fraction of blood within a measurement volume and thus encodes a modulation on the luminous flux re-emitted by the tissue and accessible for detection.

[0075] The measured modulation, corresponding to the heartbeat, contains various information accessible for determination and is generally referred to as PPG measurement. For the purpose of this application, three constituents are explained in greater detail.

[0076] Figure 2 illustrates such PPG signal. The PPG signal comprises a signal level or DC component, about 5.2xl04counts in the example shown, a primary modulation AC of the signal due to the heartbeat, which is commonly of direct interest for various applications, and a noise component, which is most easily recognized in the example shown by the profound point-to-point variability. The heartbeat is given by the periodicity of the signal (i.e. maxima, minima or zero crossing) in the signal and lies in this example in the range of about 60 beats / min (1 Hz) . The perfusion index or PI is defined by the ratio AC over DC, that is, the amplitude of the AC portion of appr . O.lxlO4counts divided by the DC portion. In the given example, the PI is approximately 1.9%.

[0077] Signals for a PPG measurement as well as for the proposed method are usually acquired over several heartbeats, e.g. , for 5 to 20 seconds, and with acquisition rates substantially greater than the anticipated maximum heart rate. Depending on the approach to determine the concentration, it is suitable to capture the heartbeat with an acquisition rate that is at least 20 times higher than the actual heartbeat to be able to resolve the higher harmonic portion of the heartbeat signal. As the heartbeat is in itself subject to variation, e.g., between app 60 beats / min to appr. 180 beats / min, it is suitable to use data acquisition rate between 50 Hz and 200 Hz. In the example depicted in Figure 2, an acquisition rate of 100 Hz with 1024 data points per trace was used. In the following , a method is illustrated to evaluate one or more PPG measurements to obtain the variability of the signal and then subsequently determine the glucose concentration . The use of certain portions of the signal , as for example the AC and noise components for glucose determination, has already been shown and are subj ect to other applications filed by the applicant . While several different methods are possible to extract the respective signals , they usually work either in the time or in the frequency domain, but may also require a significant computational effort . The present approach however can be utilized in several domains based on Parseval' s theorem. This allows not only for greater flexibility in processing , but offers different additional steps for preprocessing, thereby improving the overall evaluation quality .

[0078] Fig . 3 shows the actual PPG traces of data points #1 and #31 obtained by detecting the light scattered by the human tissue . The diagram depicts times over counts adc . The measured signals are pre-processed by using a low pass filter to remove slow transients , e . g . caused by breathing . The transient removal is also suitable to suppress endpoint discontinuities , which typically would increase the 1 / f contribution ( s ) in the frequency domain . Likewise , the current , when the light source are not emitting any light , is removed . Each trace shown is 10 seconds long, including roughly 13 heartbeats in this example . As visible from the two traces , the increasing blood sugar concentrations cause an increase in the AC portion as well as a drop in the DC portion .

[0079] More particular , the measurement for data point #1 with a lower glucose concentration comprises a DC component of appr . 1 . 6xl 04count and super imposed by an AC portion . The AC portion is approximately given by the distance between the respective maxima and minima of the heartbeats . The second trace for point #31 comprises a lower DC value of appr . 1 . 1 xlO4with a significant higher AC portion . The noise may be similar to the other , at least from the noise visible to the eye .

[0080] Figure 4 shows a typical Oral Glucose Tolerance Test , where the reference data referred to as REF original is taken from an Abott Freestyle Libre 3 system . It constitutes the reference measurement during the period of the test . Food uptake takes place at about 10 : 20 , indicated by the vertical bar and the absence of data points . Shortly afterward, both the optical and the reference measurements are performed each minute . The optical measurements are taken for 10 seconds at a time , which is long enough to obtain a suitable PPG measurement but short enough so that the glucose concentration during a single measurement is approximately constant . Each data point shown is derived from a data trace similar to the ones illustrated in Figure 3 . The data traces corresponding to point #1 and #31 depicted by the arrows are shown in the diagram of Figure 3 . They are determined using the standard variation of the respective trace divided by the average level .

[0081] Following ingestion of food, the Glucose concentration increases with time , peaking about 40 minutes after eating in this example , and decaying over the next 40 minutes . This is clearly visible in the reference curves "REF original" and "REF time shifted . " The latter curve is shifted in time by appr . 10 minutes to better correlate with the measurement values GREEN VAR TD . One potential explanation for such observation could be that the glucose concentration in the interstitial fluid reacts with a time-delay to the blood glucose concentration changes . If so , optical measurements like the one in the proposed method may be beneficial over the reference using the interstitial fluid because of the observed delay times between 5 and 25 minutes .

[0082] In accordance with the proposed method, light within the green portion of the visible spectrum is emitted onto the s kin and the scattered and subsequently reflected light from the tissue is detected . More particularly, if source and emitter are on the same side of the specimen, diffusely reflected light is detected; when they are on opposite sides , it is transmitted light .

[0083] The overall measurement takes place in this example for about 10 seconds and is repeated every minute . The detected portion of the light provides a curve similar to the one depicted in Figure 3 above . Each measurement is then processed to obtain a data point of its signal variability . The signal variability is quantified with help of the standard deviation o, o being an absolute quantity . When the standard deviation o is divided by the average signal level , referred to as DC component , one can describe the noise relative to the signal . Each data point in the Figure 4 corresponds to a measurement for 10 seconds .

[0084] The data points GREEN VAR TD shown in Figure 4 present the evaluation of the variability of the signal in the time domain for each 10s second long trace . The data has not been further processed, that is smoothed, averaged, or otherwise processed for example , but the evolution of the variability thus computed is shown over the course of the experiment as is . Consequently, the result is quite noisy . Such noise is caused by various effects , for example movement of the user during signal acquisition, environmental changes and the like . However, the curve shows a clear correlation of the signal variability with the reference measurements and with the shifted reference measurement having a clear maximum at data points 30 to 35 . Consequently, by evaluating the signal variability of a PPG measurement , one can extract the concentration of a substance , or its concentration change thereof in a particle containing liquid using optical means . This is a simple and non- invasive approach to obtain information about glucose in blood .

[0085] The proposed method with the correlation to the reference can be observed at different wavelength . Figure 5 illustrates another time- glucose concentration diagram. The optical measurements were performed in the infrared spectrum in this particular example . Similar to the previous figure , no further data processing or filtering was performed to reduce the noise , rather the signal variability was determined with the raw measurement data . However , the increase in the glucose correlation after the intake of a sugar containing liquid is clearly visible from the respective curve . Furthermore , similar to the previous Figure 4 , original reference data REF original is shifted by about 10 minutes to provide the best overlap . In other words , an increase of the blood glucose level is detected about 10 minutes earlier using the proposed method than with the reference equipment . This behavior is consistent with other experiments , indicating that optical measurements enable a faster determination of the glucose level change . The reason may be the measurement principle of the reference device , which does not measure the glucose concentration in blood directly but in the interstitial fluid .

[0086] The presently proposed principle of obtaining the concentration of a substance in a liquid containing particle is not limited to processing the measured signals and data in the time domain . Rather, the signal variability can be determined equivalently in the time or in the frequency domain . This provides a larger flexibility and offers various pre- and post-processing options , thereby improving the quality and the accuracy of the determination .

[0087] Figure 6 illustrates the Fourier transformed signal of the data point #1 and data point #31 as measured during the 10 second window depicted in Figure 3 .

[0088] After pre-processing the signals , a Fourier Transformation of the measured signals in the green spectrum ( fft green ) is performed . In the present example , the results are normalized to their respective DC value ( zero-frequency) to provide a relative comparison of the modulation amplitudes . As visible from Figure 6 , the fundamental frequency of the heartbeat at appr . 1 . 3 Hz is clearly visible in both traces for data points #1 and 31# .

[0089] However , as visible from both traces , the amplitude for the fundamental frequency for data point #1 is smaller than for data point #31 and the harmonic for data point #1 are barely visible . This is in contrast to data point #31 , where the higher harmonics can be clearly distinguished from the noise / background . The amplitude of the fundamental frequency at about 1 . 2 Hz for point #31 is about 4x greater than for point #1 corresponding to a lower glucose concentration . These aspects are also visible in measurement in the time domain of Figure 3 . As indicated on Figure 6 , the noise floor discernible in between the harmonics is also higher when the glucose concentration is increased .

[0090] It has been found that the noise floor provides additional information and impacts the overall variability as a function of the glucose concentration . Consequently, one can use the actual signal in the fundamental frequency and the harmonics , up to a certain number e . g . up to the 8th, 9th, 10th, 11thor 12thharmonics and the noise level in between the harmonics .

[0091] The actual signal is presented for the measured signal in the green spectrum in Figure 7 and the extracted noise spectrum and portion thereof in Figure 8 . The data has been decomposed into „signal" and „noise" . It has been observed that both the signal and noise exhibit a correlation to the blood glucose concentration . Therefore , the step of decomposition can be omitted and the total signal variability from the data as is can be evaluated . As it is visible from both figures , the noise and the signal are increasing in their amplitudes with increasing glucose level . Both contain a certain variability that can be calculated in the frequency domain and in the time domain using Parseval' s theorem.

[0092] Figure 9A to 9D illustrate this correlation and connects the individual signal portions , namely perfusion index and noise , both in the time and frequency domain, to the glucose concentration and the change thereof . Figure 9A illustrates the PI in the green portion of the spectrum in the frequency domain, the Figure 9B shows the respective noise portion in the frequency spectrum over time . Both show a significant correlation with the glucose concentration .

[0093] It is hence logical to surmise that the total PPG signal variability over a frequency range relevant for PPG measurement is correlated to the glucose concentration . This is illustrated in Figures 9C and 9D, respectively . Figure 9C shows the signal variability over time and its correlation with the glucose concentration against the reference measurements REG original in the time domain . Figure 9D illustrates the signal variability GREEN VAR FD in the frequency domain . Both curves are similar apart from a slight deviation at lower glucose concentrations towards the end of the measurement . Slow transients , e . g . , due to breathing have been removed from the measured signals , both in the time and frequency domain, as mentioned above . However , there is no high frequency cutoff applied to the measured signal in the time domain, in contrast to measured signal for determining the signal variability GREEN VAR FD in the frequency domain .

[0094] Hence , the implications of the use of the signal variability in the time domain ( or the frequency domain) are profound . For once , what is commonly considered noise may in fact carry valuable information about the physiology of the specimen . Further, it is computationally much easier and cheaper to evaluate the total signal variability over a frequency range of interest , e . g . , in the time domain, than it is to extract only certain features from a signal , such as the PI , in the time- and / or frequency domain .

[0095] The presently proposed method offers a new and computationally easy way to determine the concentration of a substance in a particle containing liquid . It is not necessary to extract the PI and or the noise from PPG raw data for the purpose of correlation to blood Glucose concentrations . Instead, it is sufficient to determine the cumulative signal variability in the sense of a standard deviation divided by the DC level . This can be done equivalently in the time or frequency domain . Bandpass filtering can be applied in both the time and the frequency domain .

[0096] The implication of this realization is that complicated and / or computationally expensive analyses of PPG data may be equivalently replaced with a basic determination of a standard variation and DC- level , potentially augmented by removal of slow transient and high frequency noise .

[0097] LIST OF REFERENCES

[0098] 1 detector arrangement

[0099] 10 control and evaluation circuit 11 housing

[0100] 12 light source , LED

[0101] 13 detector

[0102] 13 . 1 13 . 2 detector areas

[0103] 14 optical barrier 21 glass interface

[0104] 20 surface

[0105] 32 light path

[0106] 30 tissue

[0107] 32 light path

Claims

CLAIMS1 . Method for determining a substance concentration in a sample comprising liquid containing particles , in particular glucose in blood, wherein a refractive index of the liquid is dependent on a concentration of the substance dissolved therein and a density of particles in the liquid is substantially constant :Obtaining a first signal during a first time period with an acquisition rate that is at least two times larger than a periodicity of the volume or pressure modulation of the liquid;- Determining a variability from the obtained first signal ;- Obtaining one of : o the substance concentration from the determined variability and a reference value ; and o a change in the substance concentration from the determined variability and a reference value .2 . The method according to claim 1 , further comprising :- After obtaining a first signal , obtaining a second signal during a second time period with an acquisition rate that is at least two times larger than a periodicity of the volume or pressure modulation of the liquid;- Determining a variability from the obtained second signal ; wherein optionally, the reference value is based on the determined variability of the obtained second signal .3 . The method according to one of the claims 1 and 2 , wherein the first and / or second time period is at least 5 times smaller than a time period between the first and the second time period .4 . The method according to any of the preceding claims , wherein the reference value is given by a pre-determined variability, or a value derived by one or more variabilities .5 . The method according to any of the preceding claims , wherein the step of determining a variability from the obtained first signal and / or the second obtained signal comprises the step of :Determining a standard deviation of the obtained signal in the time domain;Determining an average signal level or DC portion of the obtained signal in the time domain;Determining the variability based on a ratio of the standard deviation and the average signal level or DC portion .6 . The method according to any of the preceding claims , wherein the step of determining a variability from the obtained first signal and / or the obtained second signal comprises the step of :Transforming the obtained first and / or second signal into the frequency domain;Determining a standard deviation of the obtained signal in the frequency domain;Determining an average signal level or DC portion of the obtained signal in the frequency domain;Determining the variability based on a ratio of the standard deviation and the average signal level or DC portion .7 . The method according to any of the claims 5 and 6 , comprising the step of .Low pass filtering the first and / or second signal in the time domain prior to determining one of the standard deviation and the average signal level or DC portion .8 . The method according to any of claims 5 to 7 , comprising at least one of the steps of :- High pass filtering the first and / or second signal in the time domain, particularly with a cut-off frequency that is lower than a frequency of the volume or pressure modulation of the liquid prior to determining one of the standard deviation and the average signal level or DC portion;- High-pass filtering the first and / or second signal to remove transients , in particular with a frequency below 2 Hz and in particular below 1 Hz and in particular below 0 , 5 Hz ;- Removing dark counts from the first and / or second signal ; and- Converting the first and / or second obtained signal into a digital signal .9 . The method according to any of the preceding claims , wherein the step of obtaining the substance concentration or the change of substance concentration comprises a quantification of deviation from a shot-noise limited system derived by the ratio of the standard deviation and the average signal level .10 . The method according to any of the preceding claims , further comprising :- Calculating a modulation to noise ratio given by the AC portion over the noise in the frequency domain, thereby quantifying an excess modulation of the signal due to the heartbeat over the system noise .11 . The method according to any of the preceding claims , wherein the liquid is blood and the modulation of the blood' s volume and / or pressure is defined by the heartbeat .12 . The method according to claim 11 , further comprising :- Calculating the heart rate from the identified AC portions in the spectrum; and / or- Obtaining a blood glucose correlation from a change of the heart rate .13 . Detector arrangement for determining a substance concentration in a sample comprising liquid containing particles , in particular glucose in blood, wherein a refractive index of the liquid is dependent on a concentration of the substance dissolved therein and a density of particles in the liquid is substantially constant , said detector arrangement comprising :- at least one light source and at least one detecting component , wherein the at least one detecting component is optically separated from the at least one light source ;- wherein said at least one light source is configured to emit light through an exit window onto a sample ; and- said detecting component is configured to detect a light component corresponding to emitted light scattered through;- a control circuit coupled to the at least one light source and at least one detecting component ; wherein the detector arrangement is configured to perform the method according to one of the preceding claims .14 . Detector arrangement according to claim 13 , wherein the control circuit is configured to control the at least one light source to emit at light signal for the first time period at a plurality of different consecutive times and obtain the signals from the at least one detecting component .15 . Detector arrangement according to claim 13 or 14 , wherein said detector arrangement comprises a plurality of photodetectors arranged in a ring shape or a quadrature shape and optionally arranged, -particularly centrally- , around the at least one light source , in particular at different distances .16 . Detector arrangement according to one of claims 13 to 15 , wherein said at least one light source comprises a plurality of optoelectronic devices being arranged with different distances to the at least one detecting component ; and optionally comprising a ring shape or a quadrature shape optionally arranged, -particularly centrally- , around the at least one detecting component .17 . Detector arrangement according to any of claims 13 to 16 , wherein the at least one light source is configured to emit light of different wavelength .18 . Detector arrangement according to any of claims 13 to 17 , wherein the at least one detecting component comprises a light filter comprising a low transmittance in a frequency spectrum different from a light spectrum emitted by the at least one light source .

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